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  <div class="section" id="mindspore-set-dump">
<h1>mindspore.set_dump<a class="headerlink" href="#mindspore-set-dump" title="Permalink to this headline">¶</a></h1>
<dl class="class">
<dt id="mindspore.set_dump">
<em class="property">class </em><code class="sig-prename descclassname">mindspore.</code><code class="sig-name descname">set_dump</code><span class="sig-paren">(</span><em class="sig-param">target</em>, <em class="sig-param">enabled=True</em><span class="sig-paren">)</span><a class="headerlink" href="#mindspore.set_dump" title="Permalink to this definition">¶</a></dt>
<dd><p>启用或者禁用target及其子节点的Dump数据功能。</p>
<p>target为Cell或Primitive的实例。请注意，此API仅在开启异步Dump功能且Dump配置文件中的 <cite>dump_mode</cite> 字段为”2”时生效。有关详细信息，请参阅 <a class="reference external" href="https://mindspore.cn/docs/programming_guide/zh-CN/master/dump_in_graph_mode.html">Dump功能文档</a> 。默认状态下，Cell和Primitive实例不使能Dump数据功能。</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>此API还在实验阶段，后续可能修改或删除。</p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<ul class="simple">
<li><p>此API只在Ascend后端的图模式有效。</p></li>
<li><p>当target是一个Cell且enabled设置为True时，Cell实例及其子Cell实例的Primitive将递归启用Dump。如果算子不是Cell实例的成员，则不会为该算子启用Dump（例如，在construct方法中直接使用的 <a class="reference external" href="https://www.mindspore.cn/docs/api/zh-CN/master/api_python/mindspore.ops.html#functional">functional 算子</a> ）。要使此API生效，请在Cell的__init__方法中使用self.some_op = SomeOp()的写法。</p></li>
<li><p>使用set_dump(Cell, True)后，Cell正向计算中的算子会被Dump，大多数反向计算（梯度运算产生的计算）不会被Dump。然而，由于图的优化，一些后向计算的数据仍然会被Dump。可以忽略文件名中包含“Gradients”的反向计算数据。</p></li>
<li><p>此API只支持训练开始前调用。如果在训练过程中调用这个API，可能不会有效果。</p></li>
<li><p>对于 <a class="reference external" href="https://www.mindspore.cn/docs/api/zh-CN/master/api_python/nn/mindspore.nn.SoftmaxCrossEntropyWithLogits.html#mindspore.nn.SoftmaxCrossEntropyWithLogits">nn.SoftMaxCrossEntropyWithLogits 层</a> ，正向计算和反向计算使用同一组算子。因此，只能看到反向计算中的Dump数据。请注意，当使用sparse=True和reduce=“mean”初始化时，nn.SoftmaxCrossEntropyWithLogits层也将在内部使用这些算子。</p></li>
</ul>
</div>
<p><strong>参数：</strong></p>
<ul class="simple">
<li><p><strong>target</strong> (Union[Cell, Primitive]) - 要设置Dump标志的Cell或Primitive的实例。</p></li>
<li><p><strong>enabled</strong> (bool) - True表示启用Dump，False表示禁用Dump，默认值: True。</p></li>
</ul>
<p><strong>支持平台：</strong></p>
<p><code class="docutils literal notranslate"><span class="pre">Ascend</span></code></p>
<p><strong>样例：</strong></p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="c1"># Please set the dump config file and environment variable before</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># running this example to actually get the dump data.</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># See the document of this API for details.</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="go">&gt;&gt;&gt;</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">mindspore.nn</span> <span class="k">as</span> <span class="nn">nn</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">mindspore.context</span> <span class="k">as</span> <span class="nn">context</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">mindspore</span> <span class="kn">import</span> <span class="n">Tensor</span><span class="p">,</span> <span class="n">set_dump</span>
<span class="go">&gt;&gt;&gt;</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">context</span><span class="o">.</span><span class="n">set_context</span><span class="p">(</span><span class="n">device_target</span><span class="o">=</span><span class="s2">&quot;Ascend&quot;</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="n">context</span><span class="o">.</span><span class="n">GRAPH_MODE</span><span class="p">)</span>
<span class="go">&gt;&gt;&gt;</span>
<span class="gp">&gt;&gt;&gt; </span><span class="k">class</span> <span class="nc">MyNet</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Cell</span><span class="p">):</span>
<span class="gp">... </span>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="gp">... </span>        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="gp">... </span>        <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="n">pad_mode</span><span class="o">=</span><span class="s1">&#39;valid&#39;</span><span class="p">)</span>
<span class="gp">... </span>        <span class="bp">self</span><span class="o">.</span><span class="n">relu1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">()</span>
<span class="gp">...</span>
<span class="gp">... </span>    <span class="k">def</span> <span class="nf">construct</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="gp">... </span>        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="gp">... </span>        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">relu1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="gp">... </span>        <span class="k">return</span> <span class="n">x</span>
<span class="go">&gt;&gt;&gt;</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">net</span> <span class="o">=</span> <span class="n">MyNet</span><span class="p">()</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">set_dump</span><span class="p">(</span><span class="n">net</span><span class="o">.</span><span class="n">conv1</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">input_tensor</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">))</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">net</span><span class="p">(</span><span class="n">input_tensor</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>

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